Henrike Heyne

This paper introduces a framework for comparing embedding spaces in molecular biology, demonstrating its utility in pre-filtering machine learning tasks and identifying differential patterns across embeddings.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

The paper compares genetic effects on disease susceptibility and progression, finding limited overlap and suggesting larger samples or different progression measures are needed.

The paper identifies 185 genome-wide significant loci associated with common diseases in a cohort of British Pakistanis and Bangladeshis with high autozygosity, focusing on recessive genetic effects.

The paper identifies genetic loci associated with female infertility through a GWAS, revealing a rare recessive mutation in TBPL2 and age-dependent risk factors.

This study investigates genetic factors influencing weight loss from GLP1-RA and bariatric surgery in 10,960 individuals across 9 biobanks, finding no strong genetic associations with GLP1-RA effectiveness but a modest link between BMI polygenic scores and reduced bariatric surgery weight loss.

This study shows that epilepsy polygenic risk scores (PRSs) can predict epilepsy risk across lifetimes and after seizure events in a large Finnish cohort.

This study evaluates polygenic scoring methods across five biobanks and finds that biobank differences outweigh method differences, with ensemble learning improving performance.

This paper introduces Paralogue Annotation, a method using homologous protein variants to predict disease-causing missense variants with high precision.

The paper demonstrates the feasibility of conducting clinical recall studies from the FinnGen biobank to collect customized clinical data from participants with Alzheimer's disease or mild cognitive disorder.

The paper identifies rare genetic variants associated with different epilepsy types, finding the most enrichment in non-acquired focal epilepsy.

Exomes of 24,248 schizophrenia cases and 97,322 controls implicate ultra-rare damaging variants in 10 genes, including GRIN2A and GRIA3, with odds ratios of 3 to 50.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

The paper analyzes homozygous and heterozygous effects of coding variants on disease in 176,899 Finns using EHR data, finding novel associations and challenging traditional dominant/recessive classifications.

The paper analyzes ultra-rare variants in epilepsy patients to identify gene-set enrichment patterns and differential impacts in neuronal gene categories.

The paper predicts functional effects of missense variants in voltage-gated sodium and calcium channels using machine learning, achieving high accuracy in distinguishing gain and loss of function variants.

The paper analyzes missense variants in 1,330 disease genes using 3D protein structures to identify mutational hotspots and their functional impact.

The paper identifies regions in protein sequences enriched for pathogenic variants using gene family alignments and demonstrates their enrichment in neurodevelopmental disorder de novo variants and ClinVar pathogenic classifications.

This study identifies ultra-rare genetic variants associated with different epilepsy types using whole-exome sequencing in 17,634 individuals.

The paper estimates the risk of five disorders from paternal-age-related de novo mutations using whole-exome-sequencing data and Danish registry data.

The paper analyzes genetic variants in 6,994 individuals with neurodevelopmental disorder with epilepsy to assess diagnostic yield and identify ultra-rare variants in associated genes.

The paper identifies de novo variants in neurodevelopmental disorders with epilepsy and finds associations with specific genes and clinical features.